function [FCNNFeature_c4, FCNNFeature_c3] = TemporalCNNFeature(vid_name, net, NUM_HEIGHT, NUM_WIDTH)

L = 10;

% Input video

filelist =dir([vid_name,'*_x*.jpg']);

video = zeros(NUM_HEIGHT,NUM_WIDTH,L*2,length(filelist));

for i = 1: length(filelist)

flow_x = imread(sprintf('%s_%04d.jpg',[vid_name,'flow_x'],i));

flow_y = imread(sprintf('%s_%04d.jpg',[vid_name,'flow_y'],i));

video(:,:,1,i) = imresize(flow_x,[NUM_HEIGHT,NUM_WIDTH],'bilinear');

video(:,:,2,i) = imresize(flow_y,[NUM_HEIGHT,NUM_WIDTH],'bilinear');

end

for i = 1:L-1

tmp = cat(4, video(:,:,(i-1)*2+1:i*2,2:end),video(:,:,(i-1)*2+1:i*2,end));

video(:,:,i*2+1:i*2+2,:) = tmp;

end

d = load('flow_mean');

FLOW_MEAN = d.image_mean;

FLOW_MEAN = imresize(FLOW_MEAN,[NUM_HEIGHT,NUM_WIDTH]);

batch_size = 40;

num_images = size(video,4);

num_batches = ceil(num_images/batch_size);

FCNNFeature_c4 = [];

FCNNFeature_c3 = [];

for bb = 1 : num_batches

range = 1 + batch_size*(bb-1): min(num_images,batch_size*bb);

images = zeros(NUM_WIDTH, NUM_HEIGHT, L*2, batch_size, 'single');

tmp = video(:,:,:,range);

for ii = 1 : size(tmp,4)

img = single(tmp(:,:,:,ii));

images(:,:,:,ii) = permute(img -FLOW_MEAN,[2,1,3]);

end

net.blobs('data').set_data(images);

net.forward_prefilled();

feature_c4 = permute(net.blobs('conv4').get_data(),[2,1,3,4]);

feature_c3 = permute(net.blobs('conv3').get_data(),[2,1,3,4]);

if isempty(FCNNFeature_c4)

FCNNFeature_c4 = zeros(size(feature_c4,1), size(feature_c4,2), size(feature_c4,3), num_images, 'single');

FCNNFeature_c3 = zeros(size(feature_c3,1), size(feature_c3,2), size(feature_c3,3), num_images, 'single');

end

FCNNFeature_c4(:,:,:,range) = feature_c4(:,:,:,mod(range-1,batch_size)+1);

FCNNFeature_c3(:,:,:,range) = feature_c3(:,:,:,mod(range-1,batch_size)+1);

end

end